A Vector-Integration-to-Endpoint Model for Performance of Viapoint Movements

Viapoint (VP) movements are movements to a desired point that are constrained to pass through an intermediate point. Studies have shown that VP movements possess properties, such as smooth curvature around the VP, that are not explicable by treating VP movements as strict concatenations of simpler point-to-point (PTP) movements. Such properties have led some theorists to propose whole-trajectory optimization models, which imply that the entire trajectory is pre-computed before movement initiation. This paper reports new experiments conducted to systematically compare VP with PTP trajectories. Analyses revealed a statistically significant early directional deviation in VP movements but no associated curvature change. An explanation of this effect is offered by extending the Vector-Integration-To-Endpoint (VITE) model (Bullock and Grossberg, 1988), which postulates that voluntary movement trajectories emerge as internal gating signals control the integration of continuously computed vector commands based on the evolving, perceptible difference between desired and actual position variables. The model explains the observed trajectories of VP and PTP movements as emergent properties of a dynamical system that does not precompute entire trajectories before movement initiation. The new model includes a working memory and a stage sensitive to time-to-contact information. These cooperate to control serial performance. The structural and functional relationships proposed in the model are consistent with available data on forebrain physiology and anatomy.Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N0014-95-1-0409

Cortical Models for Movement Control

Defense Advanced Research Projects Agency and Office of Naval Research (N0014-95-l-0409)

Isoperimetric Partitioning: A New Algorithm for Graph Partitioning

Temporal structure is skilled, fluent action exists at several nested levels. At the largest scale considered here, short sequences of actions that are planned collectively in prefronatal cortex appear to be queued for performance by a cyclic competitive process that operates in concert with a parallel analog representation that implicitly specifies the relative priority of elements of the sequence. At an intermediate scale, single acts, like reaching to grasp, depend on coordinated scaling of the rates at which many muscles shorten or lengthen in parallel. To ensure success of acts such as catching an approaching ball, such parallel rate scaling, which appears to be one function of the basal ganglia, must be coupled to perceptual variables such as time-to-contact. At a finer scale, within each act, desired rate scaling can be realized only if precisely timed muscle activations first accelerate and then decelerate the limbs, to ensure that muscle length changes do not under- or over- shoot the amounts needed for precise acts. Each context of action may require a different timed muscle activation pattern than similar contexts. Because context differences that require different treatment cannot be known in advance, a formidable adaptive engine-the cerebellum-is needed to amplify differences within, and continuosly search, a vast parallel signal flow, in order to discover contextual "leading indicators" of when to generate distinctive patterns of analog signals. From some parts of the cerebellum, such signals control muscles. But a recent model shows how the lateral cerebellum may serve the competitive queuing system (frontal cortex) as a repository of quickly accessed long-term sequence memories. Thus different parts of the cerebellum may use the same adaptive engine design to serve the lowest and highest of the three levels of temporal structure treated. If so, no one-to-one mapping exists between leveels of temporal structure and major parts of the brain. Finally, recent data cast doubt on network-delay models of cerebellar adaptive timing.National Institute of Mental Health (R01 DC02582

A Neural Circuit Model for Prospective Control of Interceptive Reaching

Two prospective controllers of hand movements in catching -- both based on required velocity control -- were simulated. Under certain conditions, this required velocity controlled to overshoots of the future interception point. These overshoots were absent in pertinent experiments. To remedy this shortcoming, the required velocity model was reformulated in terms of a neural network, the Vector Integration To Endpoint model, to create a Required Velocity Integration To Endpoint modeL Addition of a parallel relative velocity channel, resulting in the Relative and Required Velocity Integration To Endpoint model, provided a better account for the experimentally observed kinematics than the existing, purely behavioral models. Simulations of reaching to intercept decelerating and accelerating objects in the presence of background motion were performed to make distinct predictions for future experiments.Vrije Universiteit (Gerrit-Jan van Jngen-Schenau stipend of the Faculty of Human Movement Sciences); Royal Netherlands Academy of Arts and Sciences; Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

From Parallel Sequence Representations to Calligraphic Control: A Conspiracy of Neural Circuits

Calligraphic writing presents a rich set of challenges to the human movement control system. These challenges include: initial learning, and recall from memory, of prescribed stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letter-form invariance under voluntary size scaling, which entails fine control of stroke direction and amplitude during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have made rapid progress toward explaining the learning, planning and contTOl exercised in tasks that share features with calligraphic writing and drawing. This article summarizes computational neuroscience models and related neurobiological data that reveal critical operations spanning from parallel sequence representations to fine force control. Part one addresses stroke sequencing. It treats competitive queuing (CQ) models of sequence representation, performance, learning, and recall. Part two addresses letter size scaling and motor equivalence. It treats cursive handwriting models together with models in which sensory-motor tmnsformations are performed by circuits that learn inverse differential kinematic mappings. Part three addresses fine-grained control of timing and transient forces, by treating circuit models that learn to solve inverse dynamics problems.National Institutes of Health (R01 DC02852

A Two-Process Model for Control of Legato Articulation Across a Wide Range of Tempos During Piano Performance

Prior reports indicated a non-linear increase in key overlap times (KOTs) as tempo slows for scales/arpeggios performed at internote intervals (INIs) of I00-1000 ms. Simulations illustrate that this function can be explained by a two-process model. An oscillating neural network based on dynamics of the vector-integration-to-endpoint model for central generation of voluntary actions, allows performers to compute an estimate of the time remaining before the oscillator's next cycle onset. At fixed successive threshold values of this estimate they first launch keystroke n+l and then lift keystroke n. As tempo slows, time required to pass between threshold crossings elongates, and KOT increases. If only this process prevailed, performers would produce longer than observed KOTs at the slowest tempo. The full data set is explicable if subjects lift keystroke n whenever they cross the second threshold or receive sensory feedback from stroke n+l, whichever comes earlier.Fulbright grant; Office of Naval Research (N00014-92-J-1309, N0014-95-1-0409

A quantitative evaluation of the AVITEWRITE model of handwriting learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an intense relation between curvature and speed. The Adaptive Vector Integration to Endpoint (AVITEWRITE) model of Grossberg and Paine (2000) proposed how such complex movements may be learned through attentive imitation. The model suggest how frontal, parietal, and motor cortical mechanisms, such as difference vector encoding, under volitional control from the basal ganglia, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psycophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing human trajectories. The results show that model performance was variable across subjects, with an average correlation between the model and human data of 89+/-10%. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and learning of other complex sensory-motor skills would benefit from further research.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (1-R29-DC02952-01); Office of Naval Research (N00014-92-J-1309, N00014-01-1-0624); Air Force Office of Scientific Research (F49620-01-1-0397); National Institute of Neurological Disorders and Stroke (NS 33173

Lateral interception II: Predicting hand movements

D. M. Jacobs and C. F. Michaels (2006) concluded that aspects of hand movements in lateral catching were predicted by the ratio of lateral optical velocity to expansion velocity. Their conclusions were based partly on a modified version of the required velocity model of catching (C. E. Peper, R. J. Bootsma, D. R. Mestre, & F. C. Bakker, 1994). The present article considers this optical ratio in detail and asks whether it, together with a control law, predicts the (often curious) hand trajectories observed in lateral interception. The optical ratio was used to create a succession of target-position inputs for the vector integration to endpoint model of hand movements (D. Bullock & S. Grossberg, 1988). The model used this succession, initial hand position, and model parameters (fit to 60 trials) to predict hand trajectories on each trial. Predicted trajectories were then compared with observed hand trajectories. Hand movements were predicted accurately, especially in the binocular condition, and were superior to predictions based on lateral ball position, the input variable of the required velocity model. The authors concluded, as did C. E. Peper et al. (1994), that perceivers continuously couple movements to optics. Copyright 2006 by the American Psychological Association

Motion Learning and Adaptive Impedance for Robot Control during Physical Interaction with Humans

One of the hallmarks of physical interaction between humans is haptic communication, i.e. an informa- tion exchange through force signals. Humans excel in tasks that require such interaction by adapting impedance and anticipating the partner’s intentions. It is highly desirable to endow robots with similar capabilities. Recently, the robotics community renewed its interest in variable impedance control. A special emphasis is put on the development of controllers that incorporate learning as an essential element. This article combines programming by demonstration and adaptive control for teaching a robot to physically interact with a human in a collaborative task requiring sharing of a load by the two partners. Learning a task model allows the robot to anticipate the partner’s intentions and adapt its motion according to perceived forces. As the human represents a highly complex contact environment, direct reproduction of the learned model may lead to sub-optimal results. To compensate for unmodelled uncertainties, in addition to learning we propose an adaptive control algorithm which tunes the impedance parameters, so as to ensure accurate reproduction. To simplify the illustration of the concepts introduced in this paper and provide a systematic evaluation, we present experimental results obtained in physically-realistic simulation of a dyad of two planar 2-DOF robots

MS

thesisIn this research, a computerized motion planning and control system for multiple robots is presented. Medium scale wheeled mobile robot couriers move wireless antennas within a semicontrolled environment. The systems described in this work are integrated as components within Mobile Emulab, a wireless research testbed. This testbed is publicly available to users remotely via the Internet. Experimenters use a computer interface to specify desired paths and configurations for multiple robots. The robot control and coordination system autonomously creates complex movements and behaviors from high level instructions. Multiple trajectory types may be created by Mobile Emulab. Baseline paths are comprised of line segments connecting waypoints, which require robots to stop and pivot between each segment. Filleted circular arcs between line segments allow constant motion trajectories. To avoid curvature discontinuities inherent in line-arc segmented paths, higher order continuous polynomial spirals and splines are constructed in place of the constant radius arcs. Polar form nonlinear state feedback controllers executing on a computer system connected to the robots over a wireless network accomplish posture stabilization, path following and trajectory tracking control. State feedback is provided by an overhead camera based visual localization system integrated into the testbed. Kinematic control is used to generate velocity commands sent to wheel velocity servo loop controllers built into the robots. Obstacle avoidance in Mobile Emulab is accomplished through visibility graph methods. The Virtualized Phase Portrait Method is presented as an alternative. A virtual velocity field overlay is created from workspace obstacle zone data. Global stability to a single equilibrium point, with local instability in proximity to obstacle regions is designed into this system